AI-Driven Environmental Management System: Assessing Implementation, Practitioner Involvement, and Opportunities for Continuous Improvement

Artificial Intelligence (AI) is increasingly recognized as a transformative technology for enhancing Environmental Management Systems (EMSs); however, successful implementation remains constrained by organizational, technological, and governance-related challenges. While the existing literature has largely examined AI adoption, environmental management, and supply chain sustainability separately, limited attention has been given to how organizational readiness, practitioner involvement, data integrity, and responsible AI governance collectively influence AI-enabled Environmental Management System (EMS) implementation and continuous improvement. This study addresses this gap through a qualitative case study involving three focus group discussions with eighteen (18) cross-functional professionals such as quality managers, SHERQ officers, supply chain analysts, sustainability managers, HSE managers, and continuous improvement specialists. Upon conducting the third focus group discussion, sampling ceased, thereby reaching thematic saturation, and data was analyzed using thematic analysis. Using reflective thematic analysis, and the Technology–Organization–Environment (TOE) framework as a theoretical lens, the study identifies two themes: challenges related to AI implementation, operational efficiency and data integrity. The findings reveal that organizational readiness, practitioner involvement, data quality, system integration, and governance mechanisms are critical enablers of successful AI implementation, while fragmented systems, manual reporting, privacy concerns, AI reliability, and resistance to change remain significant barriers. Building on these findings, the study proposes an extended AI-driven EMS Continuous Improvement Framework that integrates TOE dimensions with responsible AI governance, emphasizing human oversight, transparency, accountability, and ethical AI use. The study presents a practical implementation pathway to guide organizations from readiness assessment and data standardization to AI governance and continuous improvement. By extending the TOE framework to the context of AI-enabled EMS, the study contributes to theoretical and practical knowledge by demonstrating that sustainable AI adoption depends not only on technological capability, but also on practitioner involvement, data integrity, and governance structures that support continuous improvement.